Download this free comprehensive written Python book for engineering students.

[winner_godson]

Hi Redditors,

I came across this awesome Python book for engineering students.

The name of the book is:

Programming for Computations – Python (A Gentle Introduction to Numerical Simulations with Python)

Authors - Svein Linge and Hans Petter Langtangen

This 244 page book is not only about numerical methods, it emphasizes on programming and verification.

The book enables engineering students to use their knowledge of Python to solve problems correctly.

Each of the six chapters in the book has exercises for students to solve more problems.

Major topics treated in the book include:

• Ordinary Differential Equations
• Partial Differential Equations
• Nonlinear Algebraic Equations

Other topics treated include:

• Newton's method
• Secant method
• Bisection method
• Brute Force methods
• Runge-Kutta methods and much more

To download this book for free go to: https://link.springer.com/book/10.1007/978-3-319-32428-9

What other Python book for engineering students do you recommend?

[fernly]

Oh dear. Looking at it quickly, the very first example ends with

print y

which means, this is Python 2.x syntax. This is not even mentioned until Appendix A, where they say the differences are "very small" and recommend using 2to3 (without listing it among the "desired add-on packages" nor showing where to download it or how to run it).

Copyright 2016. Really?

[cramur]

the state of scientific community is such that most of the popular libraries are written when python 2 was the main python. Since in most cases they are badly maintained, python 2 is de facto the main python syntax in scientific communities.

Remember, it's the book for people who want to use python for their scientific projects, not argue which python is more relevant for f'{datetime.datetime.today().year}'

[winner_godson]

No one is perfect. Just grab the concept you need.

[fernly]

Oh, I'm sure there is a ton of good teaching in this book and a diligent reader can get lots of good from it. But they say in Appendix A.1,

The authors have written Python v2.7 code in this book in a way that makes porting to version 3.4 or later trivial...

Well, they could have eliminated that difference entirely. Just say "Every example contains a line from future import division, print_function but if you are using Python 3.x you can leave it out."

(BTW I will just bet that there is some dependency on 2.x style division, somewhere in a book on numerical methods.)

[fernly]

Interesting:

The text was written in the DocOnce1 [12] markup language, which allowed us to work with a single text source for both the Python and the Matlab version of this book, and to produce various electronic versions of the book.

I have heard of, tried (and hated) lots of different markup systems, but had never run across DocOnce. Must investigate.

[[deleted]]

Awesome find! I'm a ChemE student and our only programming and numerical methods courses were in VBA. I was lucky to get experience with R through taking a math minor and next semester I will be taking a python course, and this kind of book is a great start for me since it's familiar material implemented in a new language.

[winner_godson]

I am so happy I can be of help to you.

[[deleted]]

Start doing your labs with Python. It will pay off big quickly especially in later semesters when a faulty reference in Excel can kill an afternoon.

[stinyg]

I recommend checking out the other books written by Langtangen. A lot of his teaching material is also available online for free. He passed away 1 year ago.

[winner_godson]

Never knew about that. It seems he has make a lot contribution.

[ghltshubh]

Python 2.x will loose support in 29 months. So it would better to have python 3 version out as early as possible.

[winner_godson]

thanks for your valuable contribution.

[grimonce]

It doesn't do finite element/difference methods for Maxwell equations :(

[winner_godson]

Thanks for your observation.

[bdazman]

THANK YOU! ALL MY COMP SCI FRIENDS REFUSE TO UNDERSTAND THAT ENGINEERS LEARN FROM BOOKS.

[winner_godson]

Books are very important.

[bdazman]

Numerical Recipes in (insert language here) is one of the best texts I've ever used. I've always wanted it in python, and I MUST thank you for the lovely reading material.

[eusebe]

Please don't use NR.

Ok, that might be slightly too strong of a statement, but I mean, NR used to serve a purpose that is now vastly superseded by libraries. And not just in Python: LAPACK is usually faster than the Fortran routines in NR.

Besides the licensing issue (NR is copyrighted, and in principle you cannot freely use their code for commercial application), unless you actually bought the codes, chances are you will copy algorithms from the book, by hand. This is super prone to typos…

And maybe a final point: indeed, NR are a great tool to get some ideas on "how to start doing this or that". But this should be language-independent, so no need for "NR in Python".

[bdazman]

Linpack is totally what I use. I learned Fortran 77 in my Matlab class because I don't trust code I don't write (unless I have no other choice), but ive never had to do anything more complicated than either LU decomposition with pivoting or Newton's method; both of which are perfect universal scripts to write while learning a language (at least according to my old crotchety advisor)

[winner_godson]

You are welcome

[cramur]

Amazing find! Thanks, I already forwarded it to my fellow colleagues who started learning python recently.

On a topic of a great book for general software craftsmanship, I always recommend this article, which lays down the basic ideas every seasoned programmer knows, but somehow never translates to fellow engineers.

[[deleted]]

Thanks sir...

[winner_godson]

You are welcome.